The Spanish language is one of the top 5 spoken languages in the world. Nevertheless, finding resources to train or evaluate Spanish language models is not an easy task. In this paper we help bridge this gap by presenting a BERT-based language model pre-trained exclusively on Spanish data. As a second contribution, we also compiled several tasks specifically for the Spanish language in a single repository much in the spirit of the GLUE benchmark. By fine-tuning our pre-trained Spanish model, we obtain better results compared to other BERT-based models pre-trained on multilingual corpora for most of the tasks, even achieving a new state-of-the-art on some of them. We have publicly released our model, the pre-training data, and the compilation of the Spanish benchmarks.
@article{arxiv.2308.02976,
title = {Spanish Pre-trained BERT Model and Evaluation Data},
author = {José Cañete and Gabriel Chaperon and Rodrigo Fuentes and Jou-Hui Ho and Hojin Kang and Jorge Pérez},
journal= {arXiv preprint arXiv:2308.02976},
year = {2023}
}
Comments
Published as workshop paper at Practical ML for Developing Countries Workshop @ ICLR 2020